Faris Elasha

824 total citations
30 papers, 611 citations indexed

About

Faris Elasha is a scholar working on Control and Systems Engineering, Mechanical Engineering and Civil and Structural Engineering. According to data from OpenAlex, Faris Elasha has authored 30 papers receiving a total of 611 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Control and Systems Engineering, 24 papers in Mechanical Engineering and 8 papers in Civil and Structural Engineering. Recurrent topics in Faris Elasha's work include Machine Fault Diagnosis Techniques (26 papers), Gear and Bearing Dynamics Analysis (23 papers) and Fault Detection and Control Systems (8 papers). Faris Elasha is often cited by papers focused on Machine Fault Diagnosis Techniques (26 papers), Gear and Bearing Dynamics Analysis (23 papers) and Fault Detection and Control Systems (8 papers). Faris Elasha collaborates with scholars based in United Kingdom, Nigeria and China. Faris Elasha's co-authors include David Mba, Joao A. Teixeira, Fang Duan, Xiaochuan Li, Cristobal Ruiz-Cárcel, Linghao Zhou, Mohamed Elforjani, Abdulmajid Addali, Ian Masters and Michael Togneri and has published in prestigious journals such as Renewable Energy, Sensors and Energies.

In The Last Decade

Faris Elasha

29 papers receiving 591 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Faris Elasha United Kingdom 14 481 408 151 88 52 30 611
Agusmian Partogi Ompusunggu Belgium 12 272 0.6× 323 0.8× 122 0.8× 92 1.0× 60 1.2× 49 554
Jong Moon Ha South Korea 11 327 0.7× 307 0.8× 112 0.7× 69 0.8× 32 0.6× 29 457
Yao Cheng China 13 660 1.4× 588 1.4× 242 1.6× 125 1.4× 58 1.1× 20 848
Giovanni Jacazio Italy 14 382 0.8× 330 0.8× 87 0.6× 53 0.6× 23 0.4× 78 529
Scott Alexander Billington United States 7 377 0.8× 274 0.7× 140 0.9× 93 1.1× 49 0.9× 12 519
Jae Yoon United States 11 410 0.9× 367 0.9× 131 0.9× 76 0.9× 51 1.0× 17 572
Rongjing Hong China 13 258 0.5× 416 1.0× 162 1.1× 47 0.5× 55 1.1× 22 554
Yilun Liu China 12 280 0.6× 274 0.7× 161 1.1× 94 1.1× 43 0.8× 37 514
Xian Ding China 13 404 0.8× 328 0.8× 99 0.7× 90 1.0× 75 1.4× 18 592
Alessandro Paolo Daga Italy 9 402 0.8× 217 0.5× 113 0.7× 124 1.4× 38 0.7× 23 499

Countries citing papers authored by Faris Elasha

Since Specialization
Citations

This map shows the geographic impact of Faris Elasha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Faris Elasha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Faris Elasha more than expected).

Fields of papers citing papers by Faris Elasha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Faris Elasha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Faris Elasha. The network helps show where Faris Elasha may publish in the future.

Co-authorship network of co-authors of Faris Elasha

This figure shows the co-authorship network connecting the top 25 collaborators of Faris Elasha. A scholar is included among the top collaborators of Faris Elasha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Faris Elasha. Faris Elasha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Elasha, Faris, et al.. (2021). Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review. Journal of Vibroengineering. 24(1). 46–74. 51 indexed citations
2.
Elasha, Faris, et al.. (2020). A Novel Condition Indicator for Bearing Fault Detection Within Helicopter Transmission. Journal of Vibration Engineering & Technologies. 9(2). 215–224. 16 indexed citations
3.
Elasha, Faris, et al.. (2019). Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning. Sensors. 19(14). 3092–3092. 60 indexed citations
4.
Li, Xiaochuan, et al.. (2019). Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies. 12(14). 2705–2705. 28 indexed citations
5.
Elasha, Faris, et al.. (2018). Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission. Structural Health Monitoring. 17(5). 1192–1212. 56 indexed citations
6.
Zhou, Linghao, et al.. (2017). A study on helicopter main gearbox planetary bearing fault diagnosis. Applied Acoustics. 147. 4–14. 50 indexed citations
7.
Elasha, Faris, et al.. (2017). Vibration Health or Alternative Monitoring Technologies for Helicopters. Pure (Coventry University). 1 indexed citations
8.
Addali, Abdulmajid, et al.. (2017). Demonstration of Leakage Flow Distribution of an Axial Fan inside the Tip Clearance Gap. 55th AIAA Aerospace Sciences Meeting. 2 indexed citations
9.
Elasha, Faris, et al.. (2017). Bearing Signal Separation of Commercial Helicopter Main Gearbox. Procedia CIRP. 59. 111–115. 4 indexed citations
10.
Elasha, Faris, et al.. (2017). Bearing Signal Separation Enhancement with Application to a Helicopter Transmission System. Journal of Aerospace Engineering. 30(5). 5 indexed citations
11.
Duan, Fang, et al.. (2016). Helicopter main gearbox bearing defect identification with acoustic emission techniques. Research Open (London South Bank University). 1–4. 8 indexed citations
12.
Elasha, Faris & David Mba. (2016). Improving condition indicators for helicopter health and usage monitoring systems. International Journal of Structural Integrity. 7(4). 584–595. 4 indexed citations
13.
Zhou, Linghao, et al.. (2016). Helicopter gearbox bearing fault detection using separation techniques and envelope analysis. Pure (Coventry University). 13. 1–5.
14.
Elasha, Faris, et al.. (2015). Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter Gearbox. Procedia CIRP. 38. 30–36. 36 indexed citations
15.
Elasha, Faris, David Mba, & Joao A. Teixeira. (2014). Condition Monitoring Philosophy for Tidal Turbines. International Journal of Performability Engineering. 10(5). 521–534. 13 indexed citations
16.
Ompusunggu, Agusmian Partogi, et al.. (2014). Effect of parameters setting on performance of discrete component removal (DCR) methods for bearing faults detection. PHM Society European Conference. 2(1). 4 indexed citations
17.
Elasha, Faris, Cristobal Ruiz-Cárcel, & David Mba. (2014). Bearing Natural Degradation Detection in a Gearbox: A Comparative Study of the Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis. Pure (Coventry University). 1 indexed citations
18.
Elasha, Faris, et al.. (2014). Diagnostics of worm gears with vibration analysis. 710–718. 2 indexed citations
19.
Elasha, Faris, et al.. (2014). Detection of machine soft foot with vibration analysis. Insight - Non-Destructive Testing and Condition Monitoring. 56(11). 622–626. 3 indexed citations
20.
Elasha, Faris, et al.. (2014). A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox. Journal of Failure Analysis and Prevention. 14(5). 623–636. 24 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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